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Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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Analisis Perbandingan Metode Certainty Factor Dan Dempster Shafer Theory Pada System Pakar Untuk Mendeteksi Penyakit Virus Parechovirus Pada Balita Azzahra, Bernica; Prasetyaningrum, Putri Taqwa
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research (Special Issue)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.12630

Abstract

Riset berikut tujuannya guna mengembangkan sistem pakar guna mendeteksi penyakit virus parechovirus dengan memakai perbandingan 2 metode yaitu Certainty Factor dan Dempster Shafer Theory pada pemberian solusi yang akurat. Penyakit virus Parechovirus ialah virus yang sangat berbahaya khususnya bagi balita dibawah usia 3 bulan. untuk mengatasi masalah ini pengembangan sebuah sistem pakar dirasa tepat dan efisien dalam mendiagnosis penyakit virus parechovirus. Masalah yang di hadapi meliputi tingkat kompleksitas dalam mendiagnosa dan memberikan solusi dengan tingkat kepercayaan yang tinggi. Hasil riset memaparkan bahwasanya kedua metode bisa memberi hasil yang cukup memuaskan, tetapi perbandingan kedua metode itu memberi tambahan wawasan dalam mengartikan keakuratan dan keterbatasan pengembangan sistem pakar. Hasil analisis memaparkan bahwasanya metode teori Dempster-Shafer memberi akurasi yang lebih tinggi dibanding certainty factor. Sebaliknya metode certainty factor mempunyai akurasi yang lebih rendah. Namun riset berikut juga memaparkan bahwasanya hasil maksimal bisa dicapai melalui penggabungan kedua metode ini. Oleh karenanya, riset berikut berkontribusi bagi pengembangan sistem pakar guna mendeteksi penyakit parechoviral dan memberi dasar guna keberlanjutan pengembangan di bidang ini. Kesimpulannya, penggabungan metode Certainty Factor dan Demster-Shafer telah memaparkan potensi guna mengoptimalkan keandalan dan kinerja sistem pakar untuk secara efektif menunjang diagnosis penyakit parechovirus. Hasil perhitungan kedua metode memaparkan bahwasanya metode Dempster-Schaefer memiliki taraf kepercayaan senilai 99,77% dan metode Certainty Factor memiliki taraf kepercayaan senilai 54,22%.
Forward Chaining Method in a Web-Based Bipolar Disorder Expert System Aritonang, Roselina; Prasetyaningrum, Putri Taqwa
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1694

Abstract

Bipolar disorder is a psychiatric disorder. At Argodadi Pinilih Disability Secretariat, Sedayu District, Yogyakarta Region, around 60% of people with disabilities are affected by psychiatric disorders and one of them is bipolar, one of them is depression, and suicide if initial treatment is not given to the sufferer. In addition, the cost of doing consultation with the medical specialist is not cheap.An expert system is an artificial intelligence system that is useful for diagnosing an error and as a decision-maker with the knowledge rules applied by an intelligent system that can solve problems like an expert. In making an expert system, the forward chaining method is used which aims to be able to diagnose bipolar disorder with accurate results and its utilization can be used by experts and laypeople to make an initial diagnosis of bipolar disorder.The results of this study are in the form of an expert system program that is used to diagnose bipolar disorder which can provide information related to the disease and can provide information on initial treatment of the disease. The information obtained by the consultant from the expert system is in the form of a percentage.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SUPPLIER BUKU PADA TOGAMAS MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) Karlina, Leni; Prasetyaningrum, Putri Taqwa
JMAI (Jurnal Multimedia & Artificial Intelligence) Vol. 4 No. 2 (2020): JMAI (Jurnal Multimedia & Artificial Intelligence)
Publisher : LPPM Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A selection of the best books’ supplier is very important in a bookstore, in producing books for their store. Sometimes, several books suppliers are difficult to choose, because many books suppliers from many regions, in which the selection is still manual. Such way causes a decision making which needs an accurate and precise calculation among the existed books suppliers. Therefore, researchers designed a decision support system for books suppliers’ selection, to be used by the TOGAMAS Bookstore in determining the books suppliers which will easily be selected, based on the specified criteria. This decision support system was created using the method of Simple Additive Weighting (SAW) as the tool that would help the TOGAMAS. The test was done by comparing the calculation from the TOGAMAS, with the calculation from the SAW, using 10 suppliers’ data to test the system performance. Based on the test, it was concluded that the percentage of the system performance was 50%.
Analisis Kinerja Model Support Vector Machine dalam Prediksi Kasus HIV di Indonesia Berdasarkan Data Time Series Erza, Muhammad Al-Ghifari; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7365

Abstract

Accurate predictions of HIV cases are crucial in efforts to control the epidemic effectively in Indonesia. As the number of cases and the complexity of transmission factors increase, machine learning-based prediction methods are becoming increasingly relevant. This study analyzes the performance of the Support Vector Machine (SVM) model in forecasting the number of HIV cases in Indonesia using time series data from 2012 to 2024. The CRISP-DM methodology is used as the framework for the analysis process, starting from business understanding to model deployment. The dataset used includes secondary data from the Ministry of Health, such as SIHA, national surveillance, and reports from the Directorate General of Disease Prevention and Control (Ditjen P2P). The SVM model is selected due to its ability to handle non-linear data and limited data sizes, as well as its resilience to overfitting. Model evaluation is performed using MAE, RMSE, and MAPE metrics. The results of the study show that the SVR model with an RBF kernel provides good prediction accuracy, with MAE values of 691.34, RMSE of 823.11, and MAPE of 13% on the test data. Therefore, SVM can be an effective tool to support data-driven decision-making in HIV control efforts in Indonesia.
Segmentasi Produk Pakaian Menggunakan Algoritma K-Means Clustering dan Particle Swarm Optimization untuk Strategi Pemasaran Putra, Rio Aji Hadyanta; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7367

Abstract

This research aims to analyze product segmentation in the apparel industry using the K-Means Clustering algorithm optimized with Particle Swarm Optimization (PSO) to generate accurate product segmentation that can support more effective marketing strategies for a company. The data used in this analysis were obtained from sales transactions of a clothing manufacturing company that offers various categories of apparel products. The dataset consists of 333 rows and includes transaction numbers, product types, quantities sold, and total sales values. The data were processed using the Python programming language via Visual Studio Code. The segmentation process was initially performed using the K-Means algorithm to group products, and the Elbow method was applied to determine the optimal number of clusters. The number of clusters obtained from the Elbow method was then optimized using PSO to find more optimal cluster counts and centroids. Cluster evaluation was conducted by comparing the values of several metrics, including the Davies-Bouldin Index (DBI), Silhouette Score, Sum of Squared Error (SSE), and the SSW/SSB ratio. Although the DBI increased slightly from 0.6690 to 0.6878, indicating greater similarity between clusters, the improvement in the Silhouette Score from 0.5513 to 0.5569 suggests better internal consistency within the clusters. Furthermore, the reduction in SSE from 418.52 to 313.25 indicates a tighter distribution of data within clusters, while the significant decrease in the SSW/SSB ratio from 0.4582 to 0.3075 demonstrates more clearly defined cluster boundaries and improved separation. The results of the study produced four distinct product clusters, enabling the company to implement more targeted and differentiated marketing strategies.
Optimizing Arduino-Based Laser Cut Machine Settings for Home Industry Subagyo, Ibnu Rivansyah; Prasetyaningrum, Putri Taqwa
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.3

Abstract

The rapid development of laser technology has significantly impacted various industrial sectors, particularly through the use of CNC laser cutting machines. These machines offer distinct advantages and limitations, making them suitable for processing a wide range of materials. This study aims to identify the most effective and efficient settings for a diode-based CNC laser cutting machine, specifically for cutting plywood. An experimental approach was employed, involving the design, creation, and testing of the machine. The research focused on optimizing the focus point and operational settings to achieve precise cuts. The results indicate that the optimal focus point is 12.6 mm, with the best cutting performance achieved at a speed of 500 mm/min, 30% laser power, and 7 passes. The findings suggest that this CNC laser machine is highly efficient for small-scale industries, offering affordability, ease of production, and reduced labor costs by automating multiple machines with a single computer. However, its application is limited in large-scale manufacturing due to constraints related to the Arduino-based control system and the maximum work area size.
Comparative Analysis of Machine Learning Algorithms for Sentiment Classification of Discord App Reviews Rosita, Rani; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1367

Abstract

The increasing use of digital communication applications such as Discord has generated diverse user opinions expressed through reviews on the Google Play Store. This study aims to analyze user sentiment toward the Discord application using text mining and machine learning techniques. A total of 3,000 reviews were collected through web scraping, pre-processed, labeled using a lexicon-based approach with TextBlob, and balanced using the SMOTE-Tomek method. Sentiment classification was performed into positive, negative, and neutral categories using Decision Tree, Logistic Regression, Support Vector Machine (SVM), and an Ensemble method. The Ensemble model achieved the highest accuracy of 98.67%, followed by Decision Tree (96.50%), SVM (95.83%), and Logistic Regression (90.33%). Limitations of this study include the use of lexicon-based sentiment labeling, machine translation from Indonesian to English, and initial class imbalance. Despite this strong performance, the study has limitations related to lexicon-based labeling, translation of reviews into English, and the presence of a highly imbalanced class distribution in the original dataset. Overall, the findings demonstrate that the Ensemble approach effectively improves sentiment classification accuracy and can support data-driven decision-making in application development.
Workshop Literasi Digital dan Keamanan Informasi Bagi Guru dan Siswa SMA Negeri 1 Sedayu Riadi, Imam; Shalihah, Fithriatus; Prasetyaningrum, Putri Taqwa; Robiin, Bambang
Mohuyula : Jurnal Pengabdian Kepada Masyarakat Vol 4, No 2 (2025): Desember
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/mohuyula.4.2.42-50.2025

Abstract

Perkembangan teknologi digital yang pesat menuntut peningkatan literasi digital dan kesadaran akan keamanan informasi di kalangan generasi muda, khususnya di tingkat SMA. Tujuan dari kegiatan pengabdian ini adalah untuk memberikan pelatihan mengenai literasi digital dan keamanan informasi kepada guru dan siswa SMA Negeri 1 Sedayu. Metode yang digunakan dalam pelatihan ini adalah pendekatan workshop interaktif yang meliputi ceramah, diskusi, dan simulasi praktis. Materi yang disampaikan mencakup pengenalan terhadap literasi digital, ancaman siber seperti phishing dan malware, serta cara melindungi data pribadi di dunia maya. Hasil dari kegiatan ini menunjukkan peningkatan signifikan dalam pemahaman peserta mengenai cara melindungi informasi pribadi dan mengenali ancaman siber. Berdasarkan evaluasi menggunakan pre-test dan post-test, peserta mengalami peningkatan pengetahuan mengenai literasi digital dan keamanan informasi, dengan 100% peserta mampu mengidentifikasi ancaman siber setelah pelatihan. Pelatihan ini juga berhasil meningkatkan kesadaran peserta tentang bahaya kejahatan siber, seperti cyberbullying, serta langkah-langkah pencegahan yang dapat dilakukan. Kesimpulannya, pelatihan ini berhasil mencapai tujuannya dalam meningkatkan pemahaman dan keterampilan peserta dalam menghadapi tantangan dunia digital. Diharapkan pelatihan ini dapat menjadi model untuk kegiatan serupa di sekolah lain, guna menciptakan lingkungan digital yang lebih aman dan bijak di kalangan generasi muda.
PENCEGAHAN CYBERBULLYING MELALUI PENERAPAN APLIKASI BERBASIS AI PADA SISWA SEKOLAH MENENGAH Prasetyaningrum, Putri Taqwa; Aryani, Eka; Hadi, Abdul; Raharjo, Fajar Sujud; Dewi, Amelia Kristiana; Aziza, Fadilla Maharani; Sabilla, Annisa Calza Sasa
JMM (Jurnal Masyarakat Mandiri) Vol 10, No 1 (2026): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v10i1.36046

Abstract

Abstrak: Cyberbullying merupakan masalah serius di kalangan pelajar akibat pertumbuhan komunikasi digital. Penelitian ini bertujuan untuk mencegah dan mengurangi kasus cyberbullying di SMPN 2 Godean melalui aplikasi berbasis AI "Bully Buster." Aplikasi ini memanfaatkan AI dan Natural Language Processing (NLP) untuk mendeteksi dan memantau potensi cyberbullying dalam komunikasi digital. Metode pelaksanaan meliputi pengembangan aplikasi, pelatihan bagi guru dan konselor, serta kampanye kesadaran. Mitra dalam kegiatan ini adalah SMPN 2 Godean dengan 381 siswa dan 35 tenaga pengajar. Sistem evaluasi dilakukan melalui angket pre-test dan post-test untuk mengukur peningkatan keterampilan dalam mendeteksi dan menangani cyberbullying. Hasil penelitian menunjukkan peningkatan kesadaran siswa dan staf tentang risiko serta dampak cyberbullying, dengan peningkatan keterampilan sebesar 40%. Program ini berkontribusi pada lingkungan digital yang lebih aman dan peningkatan kesejahteraan mental siswa.Abstract: Cyberbullying is a serious problem among students due to the growth of digital communication. This study aims to prevent and reduce cases of cyberbullying at SMPN 2 Godean through the AI-based application "Bully Buster." This application utilizes AI and Natural Language Processing (NLP) to detect and monitor potential cyberbullying in digital communication. The implementation method includes application development, training for teachers and counselors, and an awareness campaign. The partner in this activity is SMPN 2 Godean with 381 students and 35 teachers. The evaluation system is carried out through pre-test and post-test questionnaires to measure the improvement of skills in detecting and handling cyberbullying. The results of the study showed an increase in student and staff awareness of the risks and impacts of cyberbullying, with a 40% increase in skills. This program contributes to a safer digital environment and improved student mental well-being.
Sentiment Analysis of User Reviews for AI Applications: Evaluating SVM, Logistic Regression, and Random Forest Cahyani, Rivana Dwi; Prasetyaningrum, Putri Taqwa
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1366

Abstract

The rapid growth of AI applications such as CICI, GROK, and Gemini has resulted in a large volume of user reviews on platforms like the Google Play Store, making sentiment analysis a critical tool for understanding user perceptions. This study compares the performance of three machine learning models: Random Forest, Support Vector Machine (SVM), and Logistic Regression in classifying sentiments in 3,500 Indonesian-language reviews. A hybrid feature extraction approach, combining sentiment lexicons with TF-IDF, was applied to improve sentiment classification accuracy. The models were evaluated based on accuracy, precision, recall, and F1-score. Results indicated that all models achieved an accuracy greater than 96%, with Random Forest providing the most consistent and accurate results, achieving an overall accuracy of 99.62%. While SVM excelled in classifying positive and negative sentiments, it faced challenges with neutral reviews due to the ambiguity and overlap in sentiment expression. Logistic Regression also showed strong performance, especially on structured reviews. The findings suggest that Random Forest is the most robust and reliable model for sentiment analysis, particularly in handling diverse AI application reviews. These results offer practical insights for developers seeking to improve application performance by leveraging sentiment analysis on user feedback.
Co-Authors Abdul Hadi Adi Ronggo Wicaksono Affandi Putra Pradana Agung Supoyo Agustin, Isnaini Ahmad Iwan Fadli Ahmad Mukhlasin Ahsan, Moh Ajisari, Lanang Dian Albert Yakobus Chandra Albert Yakobus Chandra Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Ati, Gresensia Rosadelima Aziza, Fadilla Maharani Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cahyani, Rivana Dwi Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti Dewi, Amelia Kristiana dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fithriatus Shalihah Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Ibnu Rivansyah Subagyo Ibrahim, Norshahila Imam Riadi Irfan Pratama Irya Wisnubhadra Julius Bata Jumiyati Juwita Juwita Karlina, Leni Khalifah Samiih Sya'bani Sya'bani Khoirut Tamimi Kris Rahayu Kristina Andryani Larasaty, Raditha Latifah, Retno Leni Karlina Lewoema, Scholastica Larissa Zefira luky kurniawan, luky M. Anjas Leonardi M. Irfan Bahri Mita Oktafani Mu'ti, Dewi Lestari Mukti, Alphi Rinaldi Nalendra Mutaqin Akbar Nadeak, Puja Waldi Nanda, Tietan Geovanka Ningsih, Rully Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Harfin Ibna Pratama, Irfan Puja Waldi Nadeak Puja Putra, Rio Aji Hadyanta Putry Wahyu Setyaningsih Raharjo, Fajar Sujud Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Robiin, Bambang Rosita, Rani Rustiawan, Muhammad Rizqi Akfani Sabilla, Annisa Calza Sasa saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suria, Ozzi Suyoto Suyoto Viony Julianti Sipayung Wahyuningsih Wahyuningsih